Preservice Teachers’ Acceptance of Learning Management Software: An Application of the UTAUT2 Model
Bibliographic record
Abstract
Moodle also known as Learning Management System is freely available to educators. Universiti Utara Malaysia (UUM) encourages students and instructors to utilize the teaching and learning process. Moodle enables lecturer to create sequences and facilitate activities for their students, auto-marked online quizzes and exams, navigation tools, files download, grading, student progress tracking, online calendar, etc. This paper investigated the relationships between the constructs that may influence preservice teachers’ acceptance of Learning Zone (Moodle) in their learning process and assessing the influence of variation on performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, and the habit to the behavioral intention or intention of usage. The Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) is verified and found that the regression model revealed 29.5% of the variance in student’s intentions with facilitating conditions and hedonic expectancy are considerable predictors of the behavioral intention. Based on this, recommendations for prospect research in the application of UTAUT2 are discussed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".